What should community learning analytics measure?

Useful categories include access, informed choice, participation pathways, support discovery, privacy preferences, and completion of self-directed milestones.

Why is attendance not enough?

Attendance cannot explain accessibility, understanding, quality of connection, or whether an asynchronous alternative better served a participant.

From Attendance to Agency: Better Signals for Community Learning

The most useful signal is not simply whether someone arrived. It is whether the environment helped them move with greater clarity and agency.

At a glance

A privacy-aware measurement framework for understanding community learning through access, agency, connection, and support rather than attendance alone.
Useful categories include access, informed choice, participation pathways, support discovery, privacy preferences, and completion of self-directed milestones.
Attendance cannot explain accessibility, understanding, quality of connection, or whether an asynchronous alternative better served a participant.

Attendance is easy to count, but it is an incomplete picture of community learning. It cannot show whether instructions were accessible, whether a participant found a peer, whether a question was answered, or whether someone wisely chose an asynchronous path.

Measure access

Track whether people can authenticate, understand the schedule, reach the meeting destination, and use the experience on a phone or assistive technology. Access failures should be visible to administrators without exposing unnecessary personal content.

Measure agency

Look for voluntary milestones: choosing a participation path, adjusting privacy settings, saving a question, joining or leaving a directory, and identifying a next step. These signals describe use of the environment rather than social compliance.

Measure connection carefully

Connection can be reflected through opt-in greetings, peer discovery, or an intern’s own report that they found support. Avoid inferring relationship quality from message volume or time online.

Keep qualitative data governed

Reflections and assistant transcripts can reveal where instructions need improvement. They also require clear consent, role-based access, retention rules, and export controls. Private text should be excluded from routine analytics unless the participant has selected an appropriate sharing level.

Close the learning loop

Analytics have value when they lead to a documented improvement: a clearer instruction, a repaired link, a new accessibility option, or a better human handoff. Measurement should serve the community that produced it.